LiDAR can sample parts of a forest below the canopy, but it cannot see continuously through trees. A laser pulse may return from leaves and branches, lower vegetation, and the ground, allowing analysts to model canopy height, understory structure, and terrain where enough valid returns are captured. Dense or tall overstory intercepts or weakens many pulses, so below-canopy coverage is incomplete and every lower return must be classified carefully.
What a LiDAR pulse actually measures
LiDAR (light detection and ranging) measures the time it takes emitted laser energy to return after striking a surface. A single outgoing pulse can produce multiple recorded returns when it encounters several surfaces at different heights. In a forest, the sequence might include a leaf or branch in the upper crown, another return from lower foliage, and a final return from soil, rock, or fallen material.
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That sequence is a sample of the vertical structure along one laser path, not a transparent view through the stand. Foliage and woody stems can absorb, scatter, or redirect energy before it reaches lower layers. The point cloud therefore contains an uneven mixture of canopy, understory, and ground observations. Analysts infer surfaces and vegetation density from those observations; they do not receive a complete optical image of every plant and every patch of soil.
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- Upper canopy: The first intercepted returns describe crown tops and upper branches. They support canopy-height models and measurements of vertical distribution.
- Lower vegetation: Returns below the dominant crown can indicate shrubs, saplings, grasses, and lower branches, but a low return may also be part of a large tree’s crown.
- Ground: Returns that reach soil, rock, or a forest floor can be classified into a bare-earth terrain model when there are enough observations and the classification is reliable.
Because these layers overlap in height, a point’s elevation alone does not identify what produced it. A return 2 metres above a local terrain model could be a shrub, a sapling, or a low branch. The distinction depends on surrounding points, canopy geometry, terrain, and the classification method.
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Can LiDAR see through trees?
Not in the everyday sense of seeing a continuous picture through foliage. LiDAR can obtain intermittent below-canopy and ground returns when gaps in the overstory allow pulses through, but dense or tall stands leave fewer openings and fewer reliable lower observations. The result is better described as sampling beneath the canopy than as penetrating it completely.
Natural Resources Canada summarizes the sampling effect in its Federal Airborne LiDAR Data Acquisition Guideline: “Greater pulse densities increase sampling of the forest canopy, increase the likelihood of obtaining ground returns, and increase confidence in identifying ground returns in forested areas, especially in complex environments.” More pulses increase the chance of hitting an opening, but they do not eliminate occlusion or make every hidden object visible.
What “penetration” means in practice
In project specifications, penetration usually means the probability of recording useful returns from lower layers or the ground, not a guaranteed transmission through the entire canopy. A flight may produce a good terrain model in one part of a forest and sparse ground observations in a nearby patch with denser crowns, steeper slopes, or an unfavourable scan angle. Reported penetration should therefore be tied to the forest type, acquisition design, and output being evaluated.
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Bare-earth terrain
Ground-classified points can be interpolated into a digital terrain model (DTM), showing elevation of the soil or another ground surface. A DTM is strongest where enough pulses reach the ground and automated or manual classification separates those returns from low vegetation and trunks. In dense forest, the model may contain larger gaps, smoothing, or interpolation artefacts because the true surface was sparsely sampled.
A terrain model is not proof that every square metre was observed. It is a surface estimated from the available ground-classified points. If a dataset was collected for a different purpose or at low density, its point cloud may contain random and systematic errors that make it unsuitable for a forest inventory or a high-confidence ground model. The Cutfoot Sioux experimental-forest case study indexed by the USDA Forest Service specifically cautions that forestry estimates from low-density acquisitions should not be assumed reliable simply because a point cloud exists.
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Understory structure and density
Lower-stratum returns can support estimates of understory cover, height, or density when sampling is adequate and the processing method accounts for the canopy above them. These are modelled estimates, not a census of every stem. A lower return may represent a shrub, a young tree, a grass clump, or lower overstory, and areas with no returns are not automatically empty.
Campbell, Dennison, Hudak, Parham, and Butler (2018) compared relative point-density measures for understory vegetation. In their study setting, normalized relative point density explained more variation than overall relative point density: the reported coefficient of determination was R² = 0.44 versus R² = 0.14. Normalization accounts for the amount of vegetation or canopy sampled above the understory, making the metric more informative there. These are study-specific model results, not a universal accuracy rate for every forest or sensor.
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Presence or absence of suppressed vegetation
A case study in lowland Britain used leaf-on and leaf-off airborne LiDAR to map deciduous-woodland understory. The NERC Open Research Archive record reports 72% correspondence with field data for the presence or absence of suppressed trees and shrubs. It also reports 39.4 hectares—26% of that study site—mapped as understory. Those figures describe that site, acquisition, seasons, and classification approach; they should not be quoted as a general LiDAR detection rate.
Why dense and tall forests are harder
Overstory density and height
More leaves, branches, and taller crowns create more opportunities for a pulse to stop before reaching lower layers. In the 2018 understory analysis, both overstory density and overstory height were associated with lower predictive power for understory estimates. This does not mean a dense stand is unmappable; it means the same survey design that works in an open or short stand may provide weaker evidence beneath a closed, tall canopy.
Pulse density
Pulse density is the number of emitted laser pulses per unit area. Higher density gives the sensor more chances to find canopy gaps and more samples with which to identify ground returns. The needed density rises with forest and terrain complexity, so there is no single number that guarantees ground visibility in every woodland. Increasing density also increases data volume and processing cost, and still cannot resolve objects that are consistently occluded.
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Scan angle and footprint
Oblique pulses travel through more vegetation than near-nadir pulses and can produce a less even sample of the forest floor. Natural Resources Canada recommends a scan angle of no more than 20 degrees off nadir, with up to 25 degrees cited as an upper allowance, because narrower angles improve canopy penetration and support smaller footprints. The recommendation is a design guideline, not a promise of a particular ground-return percentage.
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Terrain and flight geometry
Steep slopes, ravines, irregular crowns, and changing aircraft altitude alter the relationship between the laser, vegetation, and ground. Swath overlap helps fill gaps caused by scan geometry and occlusion. The Canadian guideline recommends 50% swath overlap, with 20% as a minimum in its forest-application summary. Final specifications should consider terrain, stand complexity, desired product, and mapping scale together rather than selecting one parameter in isolation.
Season and leaf state
Leaf-off acquisition can reduce interference from deciduous foliage and may improve the chance of ground returns. Leaf-on data can be valuable for characterizing the canopy and the vegetation present during the growing season. The best season depends on whether the priority is bare-earth terrain, live understory, canopy condition, or a comparison between seasonal states. Local weather, evergreen species, leaf litter, and access constraints still affect the result.
Why sensor and platform choice changes the answer
| Configuration | Where it samples most effectively | Important limitation |
|---|---|---|
| Airborne discrete-return LiDAR | Large-area coverage and upper-canopy structure; some lower and ground returns when gaps and density permit | Recorded returns and classification may provide less detail in lower strata than a full-waveform or close-range survey in the same forest |
| Airborne full-waveform LiDAR | Vertical distribution across canopy and lower layers, with the waveform retaining more information about energy along the path | Performance remains dependent on stand structure, acquisition settings, processing, and validation; results from one study site are not universal |
| Terrestrial discrete-return scanning | Detailed sampling of trunks and lower vegetation near the scanner | Limited area and occlusion behind nearby stems; upper canopy is sampled less effectively than from above |
A comparative assessment of full-waveform airborne, discrete airborne, and discrete terrestrial laser scanning found that airborne systems detected understory in its study sites, with full-waveform airborne data performing more accurately than discrete airborne data there. Terrestrial scanning supplied more detail in lower vegetation strata near the instrument, while airborne configurations sampled upper strata more effectively. The comparison demonstrates different sampling patterns, not a universal ranking for every forest.
Ground models and understory models are not interchangeable
A ground model asks, “Where is the underlying terrain?” An understory model asks, “How much vegetation occupies a lower height range, and where?” Both may use the same point cloud, but they require different classifications, thresholds, and validation.
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The threshold trap
A common workflow subtracts a terrain model from each point and labels points within a chosen height band as understory. A height threshold is easy to explain, but it can classify the lower part of a tree crown as shrub layer or miss vegetation whose height overlaps the overstory. Terrain errors also shift normalized heights and can create apparent vegetation where the ground model is wrong.
Better interpretation
- Use terrain-normalized height together with neighbourhood structure, return information, and canopy context rather than height alone.
- Define the target layer operationally: for example, all returns between a specified height above ground, or a species- and site-specific vegetation class.
- Check whether gaps represent absence, occlusion, or insufficient sampling.
- Compare mapped classes or density estimates with field plots, independent observations, or another suitable reference dataset.
Validation matters most when the output controls a management decision, habitat assessment, restoration plan, or engineering design. A visually attractive point cloud does not substitute for reference data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to plan a survey for below-canopy information
- Define the deliverable. Decide whether you need a bare-earth DTM, canopy height, understory cover or density, individual-tree information, or a detailed vertical profile. The target layer determines the useful platform and resolution.
- Describe the forest and terrain. Record dominant species, evergreen versus deciduous cover, expected canopy height and closure, understory height, slope, and terrain complexity. These characteristics determine how much occlusion to expect.
- Select the platform and return method. Use airborne coverage for broad areas and upper-canopy structure; consider terrestrial scanning for detailed lower vegetation around accessible scanner positions; consider full-waveform airborne acquisition when vertical distribution is a central objective and the project can support its processing.
- Set acquisition geometry. Specify pulse density, footprint, scan angle, flight-line spacing, and swath overlap together. Natural Resources Canada’s forest guidance uses a scan angle of ≤20° off nadir (up to ≤25°), recommends 50% overlap (20% minimum), and states that required pulse density generally increases with forest and terrain complexity.
- Choose the season deliberately. Schedule leaf-off flights when reducing deciduous foliage is important for terrain, or leaf-on flights when live canopy and understory conditions are the subject. Document the season so later users do not confuse seasonal differences with structural change.
- Design validation before collection. Locate field plots or other reference observations that represent dense and open stands, slopes, and the understory classes of interest. Without representative validation, an accuracy figure may apply only to easy portions of the site.
- Document fitness for purpose. Preserve the sensor type, pulse density, scan geometry, acquisition date and leaf state, processing steps, class definitions, and validation results. A dataset collected for mapping roads or buildings is not automatically fit for forest inventory.
How to read a below-canopy LiDAR result
Ask what the points and surfaces actually represent before treating a map as a direct image of the forest floor.
- Check density by layer: A high overall point density can coexist with very few ground or understory returns if most pulses stop in the crown.
- Inspect gaps and edges: Missing lower returns may follow dense crowns, scan shadows, steep terrain, or swath boundaries.
- Separate observed from inferred: A continuous raster is usually interpolated or modelled between points; it is not continuous observation.
- Review classes: Confirm how ground, low vegetation, trunks, and lower overstory were distinguished.
- Look for uncertainty: Request point counts, density statistics, validation error, or confidence measures for the specific layer you intend to use.
Common claims that need qualification
“LiDAR penetrates the forest.”
It can sample beneath foliage, but penetration is incomplete and variable. State the forest type, pulse density, scan geometry, season, and the proportion or quality of lower returns being discussed.
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False. Lower overstory, trunks, shrubs, and ground can occupy overlapping height ranges. Classification must use more than a single threshold.
“A dense point cloud guarantees a good terrain model.”
False. Density improves sampling probability, yet canopy occlusion, terrain, scan angle, and classification can still leave sparse or misidentified ground observations.
“A published accuracy percentage applies everywhere.”
False. The 72% field correspondence and the 39.4-hectare (26%) understory mapping result came from one lowland-Britain case study. The R² values of 0.44 and 0.14 came from a separate 2018 analysis. Neither is a universal performance guarantee.
The practical answer
LiDAR is excellent for measuring forest structure as a sampled three-dimensional dataset. It can reveal canopy height, vertical profiles, some understory structure, and bare-earth terrain where the acquisition captures enough lower returns and the processing separates them correctly. It cannot guarantee a complete view through a dense canopy, detect every shrub or suppressed tree, or make the identity of each lower return obvious.
For a dependable below-canopy product, match the sensor and platform to the target layer, increase sampling when forest and terrain complexity demand it, control scan geometry and season, and validate the result against suitable reference data. The most defensible map is not the one with the most points overall; it is the one whose lower-layer observations and limitations are documented for the decision being made.
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